Description

I’m a Data Platform Architect with 20 years of experience in IT, including 15+ years focused on data platforms, cloud architecture and large-scale data transformation initiatives.

My expertise is centered around designing and scaling enterprise Data & AI platforms that align business needs, governance and modern data architectures. Over the years, I have worked across multiple industries including Energy, Retail, Banking, Insurance and Manufacturing, helping organizations modernize their data ecosystems and industrialize data products.

I specialize in:

⫸ Lakehouse and modern data platform architectures

⫸ Databricks, Delta Lake and distributed data processing

⫸ Multi-cloud environments (Azure, AWS, GCP)

⫸ Data governance, lineage and self-service data platforms

⫸ DataOps, platform engineering and observability

⫸ AI-enabled data platforms (RAG, LLM integration, Mosaic AI)

⫸ Domain-driven data products and Data Mesh approaches

At TotalEnergies, through a consulting engagement with CGI, I contributed to the development of enterprise data platforms within the Data Cloud Competence Center, acting as Data Platform Architect for the IT4IT business domain platform built on top of the enterprise data platform. My role combines architecture leadership, governance, platform engineering and cross-domain integration across systems such as ServiceNow, SAP, Anaplan and cybersecurity platforms.

What differentiates me is my ability to bridge:

⫸ business needs,

⫸ enterprise architecture,

⫸ and hands-on platform engineering.

I enjoy working at the intersection of strategy, architecture and execution — transforming complex enterprise challenges into scalable, governed and sustainable data platforms.

Core technologies:

Databricks • Spark • Kafka • Delta Lake • Azure • AWS • GCP • Terraform • Python • SQL • Unity Catalog • Power BI • Microsoft Fabric • CI/CD • Data Governance • Lakehouse • Data Products

Domaines d’expertise

Langues

Anglais
Bilingue ou natif
Français
Bilingue ou natif

Préférences en matière de lieu de travail

Accepte de travailler sur site

Courbevoie (jusqu’à 50 km)

Total Energies
Data-AI Platform Architect
ENERGIE
février 2021 - Aujourd'hui (5 ans et 4 mois)
Paris, France
Context : Member of the Data Cloud Competence Center (25+ engineers) delivering enterprise data platforms anddata products across business domains. Started as the first Tech Lead and evolved into Data Platform Architect for theIT4IT business platform built on top of the enterprise data platform (ODP).

• • Defined and drove the IT4IT Data Platform architecture (Lakehouse / Databricks) supporting IT business use cases across ServiceNow, Anaplan, SAP and cybersecurity domains.
• • Collaborated closely with Enterprise Architects and Foundation Platform teams to align IT4IT platform capabilities with enterprise standards, scalability and governance requirements.
• • Led the evolution roadmap of the IT4IT data platform, supporting cross-domain integrations, platform adoption and increasing data volumes while managing technical debt.
• • Defined and implemented data governance frameworks (Unity Catalog, lineage, access control, data quality), ensuring secure and governed data usage across IT domains.
• • Designed and standardized data architecture patterns for ingestion, transformation and exposure (batch and streaming), enabling consistent integration across heterogeneous enterprise systems.
• • Acted as lead data architect for the IT4IT platform, conducting architecture reviews and ensuring alignment with enterprise target architecture and platform standards.
• • Structured and implemented DataOps and platform engineering practices (CI/CD, IaC, observability), im proving reliability, scalability and developer productivity.
• • Supervised the design of data pipelines and real-time integrations (Databricks Lakeflow, Kafka), enabling domain-driven data products based on Delta Lake and Medallion architecture.
• • Established a platform-level AI integration framework (RAG, Databricks Mosaic AI) and collaborated with Product Owners and IT teams to align IT business needs with scalable platform capabilities.
Databricks Data architecture PySpark Unity Catalog intelligence artificielle
Candriam
Data Platform Architect
CAPITAL-INVESTISSEMENT
juin 2024 - octobre 2024 (4 mois)
Paris, France
Context : CTO-level advisory mission to assess and redesign the enterprise data platform, supporting scalable dataingestion, advanced analytics and secure multi-domain data management.•

• • Assessed existing data platform architecture, identifying scalability, governance and performance gaps across data domains.
• • Designed target data platform architecture based on Microsoft Fabric and Azure, enabling scalable ingestion, processing and analytics.
• • Defined data governance and security framework leveraging Microsoft Fabric capabilities, including fine-grained access control and data lineage.
• • Structured domain-driven data platform supporting data products and governed data sharing across business domains.
• • Facilitated architecture and design workshops with business and technical stakeholders, aligning platform strategy with enterprise objectives.
• • Delivered a comprehensive data platform modernization roadmap covering ingestion, processing, governance and platform evolution.
Data Governance Data architecture Data Engineering Microsoft Fabric
ARaymond
Data Platform Architect
AUTOMOBILE
janvier 2020 - février 2021 (1 an et 1 mois)
Grenoble, France
Context : Design and implementation of a scalable AWS-based data platform to centralize enterprise data, improveprocessing efficiency and enable advanced analytics across business domains.

• • Designed and implemented a cloud-native data platform on AWS, leveraging S3, Glue and Athena to enable scalable data ingestion, processing and analytics.
• • Defined data ingestion patterns for batch and near real-time data, ensuring scalable and optimized storage and processing.
• • Designed data platform standards and domain-oriented datasets, enabling consistent data management across busi ness domains.
• • Built and orchestrated data pipelines for transformation, validation and enrichment of enterprise data.
• • Enabled self-service analytics through governed datasets and query layers, improving data accessibility for business users.
• • Implemented DataOps practices (Terraform, CI/CD), industrializing platform deployment and improving relia bility.
Data architecture Data Engineering Amazon Web Services PySpark Data Lakehouse